Research Reports

Experimental Investigation Examining the Effects of Acute Exercise onImplicit Memory Function

Paul D. Loprinzi* a, Morgan Gilbert a, Gina Robinson a, Briahna Dickerson a

[a] Exercise & Memory Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi,Oxford, MS, USA.

AbstractEmerging work suggests that acute exercise can enhance explicit memory function. Minimal research, however, has examined whetheracute exercise is associated with implicit memory, which was the purpose of this study. Three separate experimental studies werecomputed (N = 120; Mean age = 21). In Experiment 1, participants were randomly assigned to either a moderate-intensity bout of acuteexercise (15-minute) or engaged in a seated control task (15-minute), followed by the completion of a word-fragmentation implicit memorytask. Experiment 2 replicated Experiment 1, but instead employed a higher-intensity exercise protocol. For Experiment 3, participants wererandomly assigned to either a moderate-intensity bout of acute exercise (15-minute) or engaged in a seated control task (15-minute),followed by the completion of a real world, 3-dimensional implicit memory task. For Experiment 1, the exercise and control groups,respectively, had an implicit memory score of 7.0 (0.5) and 7.5 (0.6) (t(38) = 0.67, p = .51). For Experiment 2, the exercise and controlgroups, respectively, had an implicit memory score of 6.9 (1.9) and 7.8 (2.4) (t(38) = 1.27, p = .21). These findings suggest that exercise,and the intensity of exercise, does not alter implicit memory from a word fragmentation task. For Experiment 3, the exercise and controlgroups, respectively, had a discrimination implicit memory index score of 0.48 (0.18) and 0.29 (0.32) (t(38) = 2.16, p = .03). In conclusion,acute exercise does not influence a commonly used laboratory-based assessment of implicit memory but may enhance real world-relatedimplicit memory function.

Keywords: consciousness, declarative memory system, physical activity, procedural memory

Europe's Journal of Psychology, 2019, Vol. 15(4), 700–716, https://doi.org/10.5964/ejop.v15i4.1837

Received: 2018-12-05. Accepted: 2019-03-17. Published (VoR): 2019-12-19.

Handling Editor: Rhian Worth, University of South Wales, Pontypridd, UK

*Corresponding author at: Exercise & Memory Laboratory, Department of Health, Exercise Science, and Recreation Management, The University ofMississippi, 229 Turner Center, University, MS 38677, USA. Tel: +662 915 5521. E-mail: pdloprin@olemiss.edu

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.

Research demonstrates that health behaviors, such as exercise, may not only have cardiometabolic protectiveeffects, but may also favorably influence neurological function (Chang, Labban, Gapin, & Etnier, 2012; Crush &Loprinzi, 2017; Etnier et al., 2016; Frith, Sng, & Loprinzi, 2017; Labban & Etnier, 2011; Loprinzi, Frith, Edwards,Sng, & Ashpole, 2018a; Loprinzi, Herod, Cardinal, & Noakes, 2013; Loprinzi & Kane, 2015; McMorris, 2016;McMorris, Sproule, Turner, & Hale, 2011; McMorris, Turner, Hale, & Sproule, 2016; Roig, Nordbrandt, Geertsen,& Nielsen, 2013; Roig et al., 2016). Notably, these findings have been observed even among young adults,which is important as memory function may start to decline in young adulthood (Salthouse, 2009). We havepreviously detailed the dearth of research on this topic among this population (young adults) and have notedthat the majority of this research has focused on episodic memory function (Loprinzi et al., 2018a). Episodicmemory is considered the conscious recall of past events or episodes from a spatial-temporal context (Loprinzi,

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Edwards, & Frith, 2017). The mechanisms through which exercise may influence episodic memory function hasbeen extensively detailed (Bherer, Erickson, & Liu-Ambrose, 2013; Loprinzi et al., 2017; McMorris, 2016;McMorris et al., 2016; Piepmeier & Etnier, 2015).

As we have recently discussed (Loprinzi & Edwards, 2017), a major gap in the literature is whether exercisecan influence implicit memory function, which includes the recall of information that was not consciously enco-ded (Slotnick, 2016). As we have previously illustrated in a systematic review (Loprinzi & Edwards, 2017), only10 published experiments have examined the effects of exercise on implicit memory, and among these, sevenwere conducted in animal models. Thus, three (Eich & Metcalfe, 2009; Padilla, Mayas, Ballesteros, & Andres,2016; Sherman, Buckley, Baena, & Ryan, 2016) human experimental studies have examined the effects of ex-ercise on implicit memory, with the findings of these studies being mixed (Loprinzi & Edwards, 2017).

Previous work (Barco, Bailey, & Kandel, 2006; Hawkins, Kandel, & Bailey, 2006) highlights common underlyingmechanisms of explicit and implicit memory function, with both memory outcomes likely influenced, in part, fromalterations in long-term potentiation, or the functional connectivity of communicating neurons. We have recentlydiscussed the potential role through which acute exercise may induce long-term potentiation (Loprinzi et al.,2017; Loprinzi, Ponce, & Frith, 2018b), likely through exercise-induced muscle spindle and vagus nerve stimu-lation. Thus, mechanistically, it is biologically plausible that acute exercise may influence both explicit and im-plicit memory.

Couched within the above, the purpose of this study was to experimentally examine the effects of acute exer-cise on implicit memory function. We address this question (i.e., does acute exercise influence implicit memo-ry?) from three sequential experimental studies conducted in our laboratory. For these three sequential experi-mental studies, data was collected over an 18-month period. The first experiment evaluates the effects of acutelower-intensity exercise on implicit memory, whereas the second experiment evaluates the effects of acutehigher-intensity exercise on implicit memory. Both of these experimental studies employ a commonly-used lab-oratory assessment of implicit memory (word fragmentation/completion task). In our third experimental study,we evaluate the effects of acute moderate-intensity exercise on implicit memory while employing a more real-world, 3-dimensional assessment of implicit memory.

Experiment 1

Method

Study Design

A two-arm, parallel-group randomized controlled experiment was employed. This study was approved by theethics committee at the University of Mississippi and participants provided written informed consent prior to par-ticipation. Participants were randomized into one of two groups (experimental and control). The experimentalgroup walked briskly for 15 minutes, while the control group engaged in a seated, time-matched computer task(Sudoku) for 20-minutes. After the exercise condition, participants sat for 5-minutes (playing Sudoku), thencompleted an affect survey (for “priming” purposes), then watched a video for 5-minutes, and then completedthe word completion task (implicit memory assessment).

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Participants

Each group included 20 participants (N = 40). This is based from a power analysis indicating sample sizesranging from 15 to 24 (d, 0.84–1.36; two-tailed α error probability, .05; 1-β error probability, .80; allocation ratio,1). This also aligns with our related experimental work demonstrating adequate statistical power (Crush &Loprinzi, 2017; Frith et al., 2017; Loprinzi & Kane, 2015; Sng, Frith, & Loprinzi, 2017). Participants were recruit-ed via a convenience-based, non-probability sampling approach. Participants (students) were eligible if theywere between the ages of 18 and 35 years. Further, participants were excluded if they:

• Self-reported as a daily smoker (Jubelt et al., 2008; Klaming, Annese, Veltman, & Comijs, 2016)

• Self-reported being pregnant (Henry & Rendell, 2007)

• Exercised within 5 hours of testing (Labban & Etnier, 2011)

• Consumed caffeine within 3 hours of testing (Sherman et al., 2016)

• Had a concussion or head trauma within the past 30 days (Wammes, Good, & Fernandes, 2017)

• Took marijuana or other illegal drugs within the past 30 days (Hindocha, Freeman, Xia, Shaban, & Curran,2017)

• Were considered a daily alcohol user (>30 drinks/month for women; >60 drinks/month for men) (Le Berre,Fama, & Sullivan, 2017)

Exercise Protocol

Those randomized to the exercise group walked on a treadmill for 15 minutes at a self-selected “brisk walk.”They were asked to select a pace as if they were late for class or to catch a bus, with a minimum speed set of3.0 mph. This exercise protocol has been shown to enhance explicit memory function (Sng, Frith, & Loprinzi,2018). The bout of exercise occurred prior to the memory assessment. Immediately after the bout of exercise,participants rested in a seated position for 5 minutes (played on-line Sudoku puzzle during this resting period).After this resting period, they completed a brief affective assessment asking them to rate (1, very unpleasant; 5,very pleasant) 25 different words (e.g., kids, outside), with these words employed in previous implicit memoryexperiments (Anderson, Carnagey, & Eubanks, 2003; Carnagey & Anderson, 2005).

After completing this brief assessment, participants watched a 5-minute video clip (The Office Bloopers Season4). They were instructed to focus closely on this video, as after the video, they would be asked to write downthree funny things from the video. The purpose of this video was to create a delay between the implicit memoryprime (indicating how the words made them feel) and the word completion task. After the video, participantscompleted a 50-item word completion task, of which 25 words (randomly sorted) came from the affective as-sessment they previously completed.

Control Protocol

Those randomized to the control group completed the same protocol as those in the exercise group, with theexception of, instead of exercising for 15-minutes followed by resting for 5-minutes, they completed a medium-level, on-line administered, Sudoku puzzle for 20-minutes. The website for this puzzle is located here:https://www.websudoku.com/

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Memory Assessment

Implicit memory was assessed using a 50-item word completion task, which has demonstrated evidence ofboth reliability (Ojemann et al., 1998) and validity (Spaan & Raaijmakers, 2011). Twenty-five words from thework of Anderson et al. (2003; Carnagey & Anderson, 2005) were used for this study. Of these 50 word frag-ments, 25 of the words were the words from the memory prime (affective assessment). The implicit memoryoutcome was the number of correctly completed words from the 25 primed words.

For each of the 50 word fragments, participants were asked to complete the word with whatever word firstcame to mind. As an example, one of the “primed” words (from the affective assessment) was “kids,” and theword fragment was “K I _ _.” Participants could have, for example, completed the word by writing “kite,” “kiss,”“kilt,” “king,” “kids,” “kind,” “kiwi,” “kink,” or “kilo.” If, however, they wrote “kids,” then they received a point. Themaximum number of points for this implicit memory, word completion task was 25, with a higher score indicativeof greater implicit memory.

Additional Assessments

As a measure of habitual physical activity behavior, participants completed the Physical Activity Vital SignsQuestionnaire, which reported time spent per week in moderate-to-vigorous physical activity (MVPA) (Ball, Joy,Gren, & Shaw, 2016). Height/weight (BMI) were measured to provide anthropometric characteristics of thesample. Lastly, before, during and after the exercise and control conditions, heart rate (chest-strapped Polarmonitor, F1 model) was assessed.

Statistical Analysis

All statistical analyses were computed in SPSS (v. 24). An independent samples t-test was used to comparethe implicit memory score across the two groups. Statistical significance was set at an alpha of .05.

Results

Demographic and behavioral characteristics of the sample are shown in Table 1. Participants, on average, were21 years, with the majority of the sample (75.0%) being female. The sample comprised multiple race-ethnicitygroups, including 58.0% being non-Hispanic white, 30.0% non-Hispanic black, and the remaining including amulti-racial or other classification. Resting heart rate was similar between the two groups (73–75 bpm), with theheart rate increasing to 130 bpm by the end of the exercise bout.

Figure 1 displays the results of the implicit memory assessment. The exercise and control groups, respectively,had an implicit memory score of 7.0 (0.5) and 7.5 (0.6) (t(38) = 0.67, p = .51). Thus, results from Experiment 1showed that acute lower-intensity exercise did not influence implicit memory function. Notably, the control grouphad slighter higher implicit memory scores.

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Figure 1. Mean and individual data points for the implicit memory task among the exercise and control groups forExperiments 1 and 2.

Discussion

Emerging work suggests that humans can form unconscious memories (Wuethrich, Hannula, Mast, & Henke,2018). Up until recently, the majority of experimental work in the exercise neurobiology field has examined theeffects of exercise on conscious, explicit memories. Recent work has emphasized the importance of additional

Table 1

Characteristics of the Study Variables across the Three Experiments

Experiment 1 Experiment 2 Experiment 3

Variable Exercise (N = 20) Control (N = 20) Exercise (N = 20) Control (N = 20) Exercise (N = 20) Control (N = 20)

Age, mean years 21.2 (2.2) 21.1 (2.1) 21.3 (2.3) 21.0 (1.4) 21.1 (1.5) 20.9 (1.6)

% Female 66.6 84.2 70.0 70.0 68.4 76.1

Race-Ethnicity, %

 White 57.1 57.9 70.0 60.0 42.1 38.1

 Black 33.3 26.3 20.0 30.0 57.9 57.1

 Other 9.5 15.5 10.0 10.0 0.0 4.8

BMI, mean kg/m2 24.9 (5.1) 23.6 (3.5) 25.5 (5.5) 26.4 (6.0) 25.0 (5.5) 26.0 (6.6)

MVPA, mean min/week 226.5 (194.5) 138.7 (89.6) 172.8 (114.1) 174.3 (143.0) 128.8 (136.0) 131.8 (130.5)

Heart Rate, mean bpm

 Resting 73.0 (11.2) 75.2 (12.4) 81.7 (14.1) 77.8 (12.0) 77.6 (9.3) -

 Midpoint 122.5 (19.3) 77.6 (9.4) 150.8 (9.7) - 120.9 (14.1) -

 Endpoint 130.5 (17.6) 75.8 (10.1) 150.6 (6.7) - 121.6 (13.6) -

 Post 82.2 (12.2) 78.5 (8.9) 93.3 (11.1) - 81.1 (10.9) -

Note. BMI = Body mass index; MVPA = Moderate to vigorous physical activity. Values in parentheses are standard deviation (SD) esti-mates. -, not assessed.

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experiments evaluating the effects of exercise on unconscious, implicit memory (Loprinzi & Edwards, 2017).This served as the motivation for Experiment 1, which evaluated the effects of acute, lower-intensity exerciseon implicit memory function. Our findings did not suggest any effect of acute exercise on implicit memory.

As noted elsewhere (Loprinzi & Edwards, 2017), only 10 published experiments have examined the effects ofexercise on implicit memory, and among these, seven were conducted in animal models, employing a fear con-dition paradigm. Six of these seven studies employed a chronic training protocol and all six demonstrated anexercise-induced enhancement effect on implicit memory. Three (Eich & Metcalfe, 2009; Padilla et al., 2016;Sherman et al., 2016) human experimental studies, all employing a word-stem completion task, examined theeffects of exercise on implicit memory. Eich and Metcalfe (2009) demonstrated that an acute marathon bout(26.2 miles) enhanced implicit memory, Sherman et al. (2016) did not demonstrate any benefits from short-du-ration (15-minute) sprinting, and Padilla et al. (2016) did not observe any implicit memory benefits from self-reported chronic exercise engagement.

Taken together, our findings align with the other mixed findings in this emerging line of inquiry. Previous experi-mental work (Chang et al., 2012; Crush & Loprinzi, 2017; Etnier et al., 2016; Frith et al., 2017; Labban & Etnier,2011; Loprinzi et al., 2013, 2018; Loprinzi & Kane, 2015; McMorris, 2016; McMorris et al., 2011, 2016; Roig etal., 2013, 2016; Sng et al., 2017) provides suggestive evidence that acute exercise can subserve explicit mem-ory function, likely as a result of enhanced neuronal excitability, alterations in neurotrophic factors, and aug-mentation of long-term potentiation, or the functional connectivity across neurons. It is conceivable that acuteexercise may also facilitate implicit memory, as there are common molecular mechanisms subserving explicitand implicit memory (Barco et al., 2006; Hawkins et al., 2006). Given the paucity of research examining theeffects of exercise on implicit memory, future work on this topic is warranted.

In conclusion, our findings do not demonstrate any evidence of implicit memory enhancement or impairmentfrom an acute bout of lower-intensity exercise. Given the paucity of research on acute exercise and implicitmemory, additional work in this area is needed. One such area of future inquiry is whether the effects of acuteexercise and implicit memory are intensity-dependent. This assertion aligns with our recent review demonstrat-ing that high-intensity exercise, when compared to lower intensity acute exercise, may more favorably influenceexplicit memory function (Loprinzi, 2018). The extent to which this may occur for implicit memory is less under-stood. Thus, Experiment 2 evaluates the effects of acute higher-intensity exercise on implicit memory function.

Experiment 2

As we have recently discussed, higher-intensity exercise (vs. lower intensity acute exercise) may more favora-bly impact explicit memory systems (Loprinzi, 2018). For example, high-intensity acute exercise that occursshortly before memory encoding may increase levels of select neurotransmitters (e.g., norepinephrine, dopa-mine, serotonin, and acetylcholine). Elevation of these neurotransmitters may activate various intracellular neu-ronal pathways (e.g., activate of kinases, such as Protein Kinase A), which in turn, may increase the phosphor-ylation of transcription factors (e.g., CREB) that subserves long-term potentiation (Loprinzi et al., 2017), a keycellular correlate of memory function (Loprinzi, 2018). Notably, some of the same underlying mechanisms thatinfluence explicit memory may also influence implicit memory function (Barco et al., 2006; Hawkins et al.,2006).

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As discussed previously, higher-intensity marathon exercise may be a potential stimulus to enhance implicitmemory function (Eich & Metcalfe, 2009). However, among the few studies on this topic, other work does notdemonstrate an implicit memory benefit from high-intensity exercise. That is, Sherman et al. (2016) did notdemonstrate any implicit memory benefits from short-duration sprinting. These mixed findings warrant future re-search. In particular, a protocol that combines both aspects of these two higher-intensity protocols may be sen-sible and feasible (i.e., shorter duration, continuous exercise). Thus, the purpose of Experiment 2 was to evalu-ate the potential effects of acute higher-intensity continuous exercise on implicit memory function.

Method

This study was approved by the ethics committee at the University of Mississippi and participants provided writ-ten informed consent prior to participation. The entire protocol for Experiment 2 was identical to Experiment 1,except for the exercise stimuli. Participants jogged on a treadmill for 15 minutes at approximately 80% of theirestimated heart rate max (220-age). After this exercise bout, participants sat and played Sudoku for 10-mi-nutes. The control group (time-matched) played Sudoku for 25-minutes.

Results

Demographic and behavioral characteristics of the sample for Experiment 2 are also shown in Table 1. UnlikeExperiment 1 (lower-intensity exercise) where heart rate increased to 130 bpm by the end of the exercise bout,for Experiment 2 (higher-intensity exercise), heart rate increased up to 150 bpm by the end of the exercisebout, which was statistically significantly higher than the exercise heart rate observed in Experiment 1 (p< .001). Notably, there were no differences in resting heart rate between the two experiments (p > .10).

Figure 1 displays the results of the implicit memory assessment for both Experiment 1 and 2. For Experiment 2,the exercise and control groups, respectively, had an implicit memory score of 6.9 (1.9) and 7.8 (2.4) (t(38) =1.27, p = .21). Similar to Experiment 1, the control group for Experiment 2 had a slightly higher mean implicitmemory score than the exercise group.

Discussion

For both Experiment 1 (lower-intensity exercise) and Experiment 2 (higher-intensity exercise), acute exercisedid not enhance implicit memory function. Although we cannot fully discount the possibility that these null ef-fects may be a result of the exercise bout duration (15-minutes), our other experimental work demonstrates thatthis exercise duration (or even less) has been shown to enhance explicit memory function (Frith et al., 2017;Haynes Iv, Frith, Sng, & Loprinzi, 2018; Jaffery, Edwards, & Loprinzi, 2018; Sng et al., 2018). Of course, howev-er, it is possible that memory type (explicit vs. implicit) may moderate the effects of exercise duration on memo-ry.

Of interest here is whether the type of implicit memory assessment may be accounting for our observed nullfindings. Thus, the purpose of Experiment 3 was to employ a more real-world, 3-dimensional assessment ofimplicit memory, and evaluate whether acute exercise influences implicit memory when assessed in this man-ner.

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Experiment 3

With a few exceptions (Brewer & Treyens, 1981; Droll & Eckstein, 2009; Pezdek, Whetstone, Reynolds, Askari,& Dougherty, 1989; Qin et al., 2014), most recognition memory tasks involve the recollection of items from com-puterized images (i.e., the encoding and recognition both occurring from computerized images). Although thisapproach may facilitate greater laboratory control, there is a trade-off as it may lack generalizability to real-world situations. Thus, it is important that, when feasible, laboratory assessments attempt to maximize this gen-eralizability in a real-world context by including testing formats that include real-world, 3-D objects. Experiment3 aims to accomplish this. That is, the aim of Experiment 3 was to examine whether acute exercise can en-hance implicit memory of 3-D objects in a real world-type context.

Method

This study was approved by the ethics committee at the University of Mississippi and participants provided writ-ten informed consent prior to participation. The entire protocol for Experiment 3 was identical to Experiment 1,with the exception of the memory assessment. A two-arm, parallel-group randomized controlled interventionwas employed. Participants were randomized into one of two groups. The experimental group walked briskly for15 minutes on a treadmill (5-minute post-exercise rest), while the control group engaged in a 20-minute seatedtask. After this, the implicit memory task was commenced.

Memory Assessment and Procedures

The laboratory set-up included three adjacent 10’ × 10’ rooms, each separated by a wall (6’ in height). SeeFigure 2 for a schematic of the laboratory set-up. Participants entered the laboratory through the door entrance,which is near Unit 1 (Figure 2). They sat in the chair in Unit 1 to complete the consent document and demo-graphic survey. At this time, the participant were asked to give the researcher their cellphone, which was re-turned to the participant after the study was completed. This was performed to ensure that the participant didnot distract themselves with their phone during the experimental protocol.

Figure 2. Schematic overview of the laboratory set-up for Experiment 3.

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After going over the consent document and demographic survey, participants then exercised for 15-minutes onthe treadmill in Unit 1 (if in the exercise group). After this bout of exercise, the participant sat back in the chair inUnit 1 for 5 minutes. During this 5-minute resting period, they sat quietly and completed a medium-level, on-lineadministered, Sudoku puzzle (same as the control group).

After this 5-minute resting period, the participant got up from the chair in Unit 1 and walked over to Unit 3 andsat in the chair in front of the desk. They were instructed to sit in the chair quietly for 2-minutes. They were notgiven any tasks during this period and were told to sit quietly for a few minutes. The computer at their desk wasturned off, so they were not be able to be distracted by any electronic device (e.g., computer or cell phone).After this 2-minute period, the researcher signaled them to come back to Unit 1. When returning to the chair inUnit 1, they completed another on-line Sudoku puzzle for 5-minutes. After this, they completed the incidentalmemory task.

The incidental memory task was completed on the computer in Unit 1. They observed a series of 18 objects onthe computer, with one object appearing per slide. They advanced through the 18 slides at a self-directed pace.For each image, they were asked to indicate if they saw the image when they were in Unit 3 (completed viapaper-and-pencil). They were told to focus their attention on the object, rather than its appearance (e.g., appa-rent size or distance). The image was a close-up picture of the object, with this picture taken in a different envi-ronment. For each image, participants selected one of three options via a paper-pencil document. The possibleoptions include: “remember,” “know,” and “new.” See Table 2 for the description of these options.

Table 2

Response Options for the Implicit Recognition Memory Task (Experiment 3)

Response Option Description

Remember “Remember” is the ability to become consciously aware again of some aspect or aspects of what happened or what was experienced

at the time you were exposed to the object (e.g., aspects of the physical appearance of the image, or something that you were

thinking at the time of viewing the image). In other words, the “Remember” images should bring back to mind a particular association,

image, or something more personal from the time of the study, or something about its appearance or position.

Know “Know” responses should be made when you recognize seeing the image when in the other unit, but you cannot consciously recollect

anything about its actual occurrence or what happened or what was experienced at the time of its occurrence.

New “New” responses would be if you are certain that you did not previously see the image when in the other unit.

Nine of the 18 images that they were tested on were objects that were placed in Unit 3. These nine objectswere placed on the desk they were sitting at (in Unit 3), the table near the desk, or on the floor next to thetreadmill. Of the nine objects placed in Unit 3, three were objects that we classified as “context- typical object,”three were “context-atypical objects,” and three were “context-unfamiliar objects.” See Table 3 for a descriptionof these objects. One object from each of these categories was placed on the desk, table, and floor in Unit 3.The other nine images that they viewed on the computer screen were objects that were not in Unit 3, but fallwithin these three categories. That is, for each object placed in the experimental room (Unit 3), we selected asimilar object from the same object-type (i.e., context-typical, context-atypical, or unfamiliar). The 18 images/slides were randomly ordered.

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Table 3

Description of the Objects Used in Experiment 3

Context-Typical Objects Context-Atypical Objects Context-Unfamiliar Objects

Binder (3-ring) Spatula Jawzrsize

Textbook Hanger Miniaturized HJ Scraper

Stapler Sunscreen lotion container (8 oz) Wrist attachment device

After the completion of the study, participants completed a brief survey confirming the categorization of theseobjects. These questions are shown in Table 4. Additionally, after the conclusion of the study, participants com-pleted a brief survey assessing their anticipation of the memory task. See Table 5 for the specific questionsused to evaluation their anticipation of the memory protocol.

Table 4

Survey Questions Assessing Participant Confidence, Familiarity, and Typicality of Each Object (Experiment 3)

Naming Confidence Context-Typical Objects Context-Unfamiliar Objects

“How confident are you that you know the specific

name of this object.”

“How typical do you consider this object in the

context of a University.”

“How often do you encounter this object in your

daily live?”1 = not at all confident;2 = little confidence;3 = neither;4 = somewhat confident;5 = very confident

1 = not typical at all;2 = somewhat typical;3 = neither;4 = typical;5 = very typical

1 = never;2 = rarely;3 = occasionally;4 = very frequently;5 = always

Table 5

Questions Used to Classify Anticipation of the Memory Task (Experiment 3)

Question Response Options

“When I was in Unit 3, I suspected that I would be tested on the

identities of the objects on the desk, table and/or floor.”

1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; and 5 = strongly agree.

“When I was in Unit 3, I made an effort to memorize the identities of

the objects on the desk, table and/or floor.”

1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; and 5 = strongly agree.

Data Reduction for the Memory Task

For the memory recognition assessment, a hit rate score was calculated as a rate of correctly indicating thatthey previously saw the image when in Unit 3 (i.e., that they “remembered” or “knew” they saw the image). Afalse rate score was calculated as a rate of incorrectly indicating that they previously “remembered” or “knew”seeing an image that was not present in Unit 3. Lastly, the discrimination index was calculated as “hit rate—false rate.”

Results

Table 1 describes the characteristics of the sample. Similar to Experiment 1, for Experiment 3, heart rate in-creased from the 70’s (bpm) to the 120’s (bpm).

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Regarding the categorization of the objects, which, as shown in Table 3, were categorized into one of the fol-lowing three categories: context-typical, context-atypical, and context-unfamiliar. Table 4 reports the surveyquestions that were employed to evaluate how confident they were in naming the object, how typical they con-sider the object in the laboratory setting, and how often they encountered the object in their daily life. Regardingtheir confidence ratings, the mean (standard deviation, SD) confidence ratings for typical, atypical, and unfami-liar objects, respectively, were 4.45 (0.55), 4.86 (0.44), and 2.20 (0.7). This confirms that our selected objectswere categorized appropriately. Regarding how typical participants considered the object in the laboratory set-ting, the mean (SD) typical ratings for typical, atypical, and unfamiliar objects, respectively, were 4.0 (0.51),2.64 (0.99), and 2.17 (0.83). This confirms that our selected objects were categorized appropriately in the con-text of how typical they thought the object was for this research setting. Lastly, participants indicated how oftenthey encountered the object in their daily lives. The mean (SD) ratings for typical, atypical, and unfamiliar ob-jects, respectively, were 3.18 (0.60), 3.65 (0.53), and 1.49 (0.43). This confirms that our selected objects werecategorized appropriately for how often they encounter these objects in their daily life. As shown in Table 5, weasked two remaining questions at the end of the visit, including whether the participant anticipated that the ob-jects in the room were related to the memory task and whether they attempted to memorize these objects. Forboth of these items (Table 4), response options ranged from 1 (strongly disagree) to 5 (strongly agree). Themean (SD) score for whether they anticipated that they would be asked to recall the object and whether theytried to memorize the object, respectively, were 1.62 (1.0) and 1.67 (1.0). Thus, participants “strongly dis-agreed” to “disagreed” that they anticipated these object were part of the memory protocol and that they tried tomemorize the objects.

Table 6 displays the hit rate, false rate, and discrimination index across the exercise and control groups for thecontext-typical, context atypical, and context-unfamiliar items. The discrimination index was statistically signifi-cantly higher for the exercise (vs. control) group for context typical objects (0.44 vs. 0.23, p = .03) and for theoverall discrimination index (0.48 vs. 0.29, p = .03).

Figure 3 displays the individual participant discrimination index scores across the two groups. As indicated, forexperiment 3, the exercise and control groups, respectively, had a discrimination implicit memory index score of0.48 (0.18) and 0.29 (0.32) (t(38) = 2.16, p = .03).

Table 6

Mean (SD) Hit Rate, False rate, and Discrimination Index (d’) between the Exercise and Control Groups

Group

Context-Typical Context-Atypical Context-Unfamiliar

Hit-Rate d’ Hit-Rate d’ Hit-Rate d’ Overall Hit Rate Overall False Rate Overall d’

Exercise 0.63 (0.31) 0.44 (0.26) 0.61 (0.22) 0.42 (0.17) 0.75 (0.29) 0.57 (0.31) 0.67 (0.21) 0.18 (0.16) 0.48 (0.18)

Control 0.57 (0.30) 0.23 (0.32) 0.65 (0.28) 0.31 (0.30) 0.68 (0.37) 0.34 (0.52) 0.63 (0.21) 0.34 (0.29) 0.29 (0.32)

p-value .53 .03 .66 .16 .50 .11 .64 .05 .03

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Figure 3. Mean and individual discrimination index scores across the exercise and control groups (Experiment 3).

Discussion

The purpose of this experiment was to extend previous work that has mostly focused on: 1) recognition memo-ry tasks using a computerized approach for single dimension objects, and 2) the effects of exercise on explicitmemory function. Specifically, we evaluated the potential effects of a moderate-intensity bout of aerobic exer-cise on the implicit memory recognition of 3-dimensional objects that were not explicitly encoded. Our experi-mental results demonstrate that a brief, moderate-intensity bout of aerobic exercise was effective in enhancingimplicit memory. Notably, however, this overall enhancement effect appears to be driven by differences ob-served for the context-typical objects. Further, the overall differences in the discrimination index also appear tobe driven by a lower false rate (vs. higher hit rate) among the exercise group (vs. control group).

Our findings align with other emerging work in this field showing that acute exercise may subserve explicit andimplicit memory function (Loprinzi & Edwards, 2017; Loprinzi et al., 2018a). There are overlapping mechanismsand pathways influencing explicit and implicit memory, which have been described elsewhere (Barco et al.,2006). Acute exercise may influence both of these memory types by, for example, altering neuronal excitabilityand activating pathways involved in long-term potentiation and synaptic plasticity (Loprinzi & Edwards, 2017;Loprinzi et al., 2017; Loprinzi & Frith, 2019; Piepmeier & Etnier, 2015).

These findings also have numerous implications, as much of the information individuals acquire in their dailylives are done incidentally. For example, individuals are not always actively trying to memorize or encode spon-

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taneous events that occur throughout the day, faces they encounter, or objects exposed to during the day. For-tunately, emerging work suggests that conscious perception is not mandatory for memory formation, particularlyspatial-based episodic memories (Wuethrich et al., 2018). Encouragingly, our findings suggest that acute walk-ing, which, in theory, could occur at nearly any point throughout the day, may help to facilitate the retrieval ofimplicit memories. An interesting follow-up experiment would be to evaluate whether acute walking, to occurduring the encoding of the implicit memory (as opposed to priori to encoding, which was the case in the presentexperiment), may also help to facilitate implicit memory retrieval. If true, this would have greater real-world ap-plications to the potential for exercise to enhance implicit memory, as, in theory, more implicit events are likelyto occur during ambulation.

General Discussion

Experiment 1 (lower-intensity exercise) and Experiment 2 (higher-intensity exercise) did not demonstrate anyeffect of acute exercise on implicit memory. However, Experiment 3 provided suggestive evidence that acutemoderate-intensity exercise may improve implicit memory when assessed in a more real-world way.

Subconscious encoding of material, in theory, may be more enhanced for real-world type objects. If true, thismay, in part, explain the notable differences across our experiments. Further, research demonstrates that un-conscious relational encoding of multiple objects may depend on the hippocampus (Duss et al., 2014; Hannula& Greene, 2012; Reber, Luechinger, Boesiger, & Henke, 2012), a critical brain structure that is influenced (in-creased neuronal activity) by acute exercise (Rendeiro & Rhodes, 2018). Perceptual implicit processing mayoccur from priming existing memories (Kuldas, Ismail, Hashim, & Bakar, 2013). Perhaps our real-world implicitmemory task was more robust in priming such memories, and thus, facilitated the role of acute exercise in suchimplicit processes. Clearly, additional work in this area is needed. If confirmed by future experimentation, thencritical thought will be needed to evaluate the mechanism through which acute exercise may enhance implicitmemory.

Limitations of these experiments is the relatively small, homogenous samples evaluated. Thus, larger samplesin broader populations may help increase the generalizability of our findings. Further, although Experiment 2employed a higher exercise intensity than Experiment 1 (20 bpm higher heart rate), this may not have beenlarge enough of a physiological change to elicit improvements in memory function. Strengths, however, includethe experimental approach of three integrated studies.

In conclusion, these novel experiments demonstrate that acute exercise does not influence a commonly usedlaboratory-based assessment of implicit memory but may enhance real world-related implicit memory function.Future confirmatory work on this paradigm is warranted. Such work should also evaluate the potential effects ofother exercise parameters (e.g., duration, temporality, setting) on implicit memory.

Funding

The authors have no funding to report.

Competing Interests

The authors have declared that no competing interests exist.

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Acknowledgments

The authors have no support to report.

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About the Authors

Paul Loprinzi, PhD, is an associate professor in the Department of Health, Exercise Science and Recreation Managementat the University of Mississippi. He is the director of the Exercise & Memory Laboratory.

Morgan Gilbert is an undergraduate researcher in the Exercise & Memory Laboratory at the University of Mississippi.

Gina Robinson is an undergraduate researcher in the Exercise & Memory Laboratory at the University of Mississippi.

Briahna Dickerson is an undergraduate researcher in the Exercise & Memory Laboratory at the University of Mississippi.

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PsychOpen GOLD is a publishing service byLeibniz Institute for Psychology Information (ZPID),Trier, Germany. www.leibniz-psychology.org